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Options and Insurance for Planning
Abstract
In evaluating projects using cost-benefit analysis, the option value of such projects and the insurance they provide is likely to alter both the costs and the benefits substantially.
... For an insurance company, the insurance clerk not only needs to master the coverage and amount of each type of insurance but also requires the insurance clerk to master each customer's purchase of insurance. rough these professional knowledge and situations, insurance salesmen need to do their best to sell insurance [5][6][7]. It can be seen from the above description that the data of insurance business is numerous, multisource, and complex, which is a difficult task for insurance business, that consumes a lot of human and material resources [8]. ...
Insurance marketing is a discipline that maximizes the benefits between policyholders and insurance companies. A big data-driven approach combined with insurance promotions can leverage a wealth of empirical data to develop new customers, motivate existing customers to engage in more activities, and retain existing customers. Insurance business involves a wide variety of scopes and types, and it is labor-intensive and resource-intensive to rely solely on insurance business personnel to process these tedious data. The data-driven approach can find correlations and perform automatic prediction matching according to the characteristics of insurance business data, which saves a lot of time and labor costs. This research uses data mining method and neural network method to mine insurance business data and predict insurance business. This method can accurately capture factors such as the type of insurance business and the amount of the policyholder. The research results show that data mining technology and neural network method have high accuracy and feasibility in predicting insurance business, the prediction error is within 2.38%, and the linear correlation exceeds 0.96. The method used in this study has high accuracy both in terms of new customers and retention of old customers.
Standard techniques of cost effectiveness analysis measure a technology’s benefits in terms of expected life years (or quality-adjusted life years) gained at today’s life expectancies. However, this approach ignores the gains which derive from the possibility that a health technology allows an individual to survive long enough to benefit from other technological innovations which raise life expectancy (and quality of life) in the future. Borrowing a term from the finance literature, we refer to this source of value as the “option value” of innovation. We explain where this value comes from and how to calculate it in a variety of standard cost effectiveness analysis contexts. We provide a proof-of-concept using the example of the drug tamoxifen, which delayed the onset of breast cancer for some patients until more effective adjuvant treatment was available. We
find that incorporating option value can increase the conventionally estimated value of tamoxifen with better adjuvant treatment by nearly a quarter (from 248,000 for those who initiated tamoxifen in 1999). We expect similar results for other drugs in therapeutic areas of rapid technological advancement.
Economists think of medical innovation as a valuable but risky good, producing health benefits but increasing financial risk for consumers and healthcare payers. This perspective overlooks how innovation can lower physical risks borne by healthy patients facing the prospect of future disease. We present an alternative framework that accounts for all these sources of value and links them to the value of healthcare insurance. We show that any innovation worth buying reduces overall risk and generates positive insurance value on its own. We conduct a stylized numerical exercise to assess the potential empirical significance of our insights. Our calculations suggest that conventional methods meaningfully understate the value of historical health gains and disproportionately undervalue treatments for the most severe illnesses, where physical risk to consumers is the costliest. These calculations also suggest that the value of physical insurance from new technologies may exceed the financial spending risk that they pose.
We compare health care spending in the USA to other industrialized countries and find that payment rates for hospitals, physicians, and drugs are generally much higher in the USA than they are in other industrialized countries while the quantity of services – as measured by the number of physician visits, hospital days and prescriptions filled per capita – is relatively similar across countries. We then explore policy initiatives designed to control payment rates and volume of services and review the success and failures of these initiatives. Within the USA, the private sector pays significantly higher rates for hospital and physician services and drugs than the public sector. Thus, if the USA is going to reduce health care spending, it may be necessary to begin by reducing payment rates in the private sector. Options to achieve this goal are presented.
Purpose
– The purpose of this paper is to propose a risk-based framework to estimate the option value of infrastructure investment, accounting for the stochastic behavior of both financial and physical (engineering) variables.
Design/methodology/approach
– This study uses a real-options approach and computes the optimal investment dates and option values using Least Squares Monte Carlo, both the original Longstaff – Schwartz algorithm and the constrained Least Squares approach of Le tourneau – Stentoft.
Findings
– Real-option value for infrastructure investment is substantial. It is beneficial to model jointly financial and engineering risks to better understand the timing and real-option value of infrastructure investment. The analysis further shows which variables are option value drivers.
Research limitations/implications
– Future work could integrate financing constraints into the model, consider path dependency in the physical state variables or integrate sovereign risk, expropriation risk, operational risk or other project risks.
Practical implications
– Financial practitioners and investment managers interested in infrastructure risk finance or project finance will benefit from a novel framework to analyze infrastructure investments in which engineering and financial risks interact in a tractable way.
Social implications
– Public decision-makers will benefit from a better understanding of what determines the value of infrastructure investments, how real-option value affects optimal investment timing and how both are determined by financial and engineering risks.
Originality/value
– The analysis considers financial and engineering risks in a single framework to better understand option value in infrastructure investment. The framework and findings are useful both to risk finance and project finance practitioners and investors as well as engineers and public sector decision-makers.
State agencies and private historical organizations frequently acquire historical sites with unknown characteristics. In this
paper, we provide two approaches to evaluating the preservation decision. In the first approach, we show that a historical
site which is not permanently preserved provides citizens with a certain flexibility whose value can be measured as an option
on the maximum between the current real estate value and the preservation value. In the second approach, we assume that the
organization has an infinite planning horizon and chooses the optimal sale date. Using a contingent valuation estimate of
the public's willingness to pay for preservation of a specific historical site and the real estate price, we provide simulation
values of the preservation option value and the optimal stopping rule.
A question has arisen concerning the relationship between the financial theory of options and the concept of option value developed in the literature on environmental preservation. This article presents simple models in each approach and then demonstrates their equivalence. The conventional, differing interpretations of results in the two approaches are also shown to be consistent though consideration of the difference can lead to a deeper understanding of the concept of option value.